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| Δια-ποσοστημοριακό διάγραμμα (Cross-Quantilogram)× | Μέθοδος Στιγμών για Παλινδρόμηση Ποσοστημορίων× | |
|---|---|---|
| Πεδίο | Οικονομετρία | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 2012 | 2004 |
| Δημιουργός≠ | Oliver Linton and Yoon-Jin Whang | Roger Koenker and colleagues |
| Τύπος≠ | Correlation measure | Distribution regression |
| Θεμελιώδης πηγή≠ | Linton, O., & Whang, Y. J. (2012). Quantile comparisons of time series data. Journal of Econometrics, 170(2), 242-257. link ↗ | Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74-89. DOI ↗ |
| Εναλλακτικές ονομασίες≠ | — | GMM quantile regression |
| Συναφείς | 3 | 3 |
| Σύνοψη≠ | The cross-quantilogram extends the cross-correlogram concept to quantile pairs of two time series, measuring dependence at different quantile levels. Introduced by Linton and Whang (2012), it captures how shocks at specific quantile levels in one series relate to movements in another, enabling asymmetric dependence analysis. This approach is particularly valuable when downside and upside risk correlations differ materially. | Method of Moments Quantile Regression combines moment-based estimation (GMM) with quantile regression to estimate distribution parameters while handling endogeneity, panel structure, and dynamic relationships. Introduced by Koenker (2004) and developed by Machado and Mata (2005), it enables distributional analysis (not just mean regression) in complex settings like dynamic panels and instrumental-variable contexts. This approach is powerful for understanding heterogeneity in treatment effects and policy impacts. |
| ScholarGateΣύνολο δεδομένων ↗ |
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